The invention relates to analyzers for conducting a range of clinical assays.
Automated analyzers are a fixture in the clinical laboratory. Assays that used to require significant manual human involvement are now handled largely by loading samples into an analyzer, programming the analyzer to conduct the desired tests, and waiting for results. The range of analyzers and methodologies in use is large. Some examples include spectrophotometric absorbance assay such as end-point reaction analysis and rate of reaction analysis, turbidimetric assays, nephelometric assays, radiative energy attenuation assays (such as those described in U.S. Pat. Nos. 4,496,293 and 4,743,561 and incorporated herein by reference), ion capture assays, colorimetric assays, fluorometric assays, electrochemical detection systems, potentiometric detection systems, and immunoassays. Some or all of these techniques can be done with classic wet chemistries; ion-specific electrode analysis (ISE); thin film formatted “dry” chemistries; bead and tube formats or microtiter plates; and the use of magnetic particles. U.S. Pat. No. 5,885,530 provides a description useful for understanding the operation of a typical automated analyzer for conducting immunoassays in a bead and tube format and is incorporated herein by reference.
Despite the array of different analyzer types and assay methodologies, most analyzers share several common characteristics and design features. Obviously, some measurement is taken on a sample. This requires that the sample be placed in a form appropriate to the measurement technique. Thus, a sample manipulation system or mechanism is found in most analyzers. In wet chemistry devices, sample is generally placed in a sample vessel such as a cup or tube in the analyzer so that aliquots can be dispersed to reaction cuvettes or some other reaction vessel. A probe or proboscis using appropriate fluidics such as a pumps, valves, liquid transfer lines such as pipes and tubing, and driven by pressure or vacuum are often used to meter and transfer a predetermined quantity of sample from the sample vessel to the reaction vessel. The sample probe or proboscis or a different probe or proboscis is also often required to deliver diluent to the reaction vessel particularly where a relatively large amount of analyte is expected or found in the sample. A wash solution and process are generally needed to clean a nondisposable metering probe. Here too, fluidics are necessary to accurately meter and deliver wash solutions and diluents.
In addition to sample preparation and delivery, the action taken on the sample that manifests a measurement often requires dispensing a reagent, substrate, or other substance that combines with the sample to create some noticeable event such as florescence or absorbance of light. Several different substances are frequently combined with the sample to attain the detectable event. This is particularly the case with immunoassays since they often require multiple reagents and wash steps. Reagent manipulation systems or mechanisms accomplish this. Generally, these metering systems require a wash process to avoid carryover. Once, again, fluidics are generally a central feature in the conduct of these operations.
Other common systems elements include measurement modules that include some source of stimulation together with some mechanism for detecting the stimulation. These schemes include, for example, monochromatic light sources and calorimeters, reflectometers, polarimeters, and luminometers. Most modem automated analyzers also have sophisticated data processing systems to monitor analyzer operations and report out the data generated. Numerous subsystems such as reagent cooler systems, incubators, and sample and reagent conveyor systems are also frequently found within each of the major systems categories already described.
An assay failure, as the term is used in this specification, occurs when an assay result is obtained that is believable yet unacceptably inaccurate and if used as the sole source of clinical data would result in an improper clinical choice (i.e., treatment). Inaccuracies or a loss of precision can occur as a result of an almost endless list of factors such as mechanical noise or even inefficient programming protocols. Most of these are relatively easy to address. However, with analyte concentrations often measured in the μg/dL, ng/dL, or even MlU/L range, special attention must be paid to sample and reagent manipulation systems and those supporting systems and subsystems that affect the sample and reagent manipulation systems. The sample and reagent manipulation systems require the accurate and precise transport of small volumes of liquids and thus generally incorporate extraordinarily thin tubing and vessels such as those found in sample and reagent probes. Most instruments require the simultaneous and integrated operation of several unique fluid delivery systems, each one of which is dependent on numerous parts of the hardware/software system working correctly. Some parts of these hardware/software systems have failure modes that may occur at a low level of probability. A defect or clog in such a probe can result in wildly erratic and inaccurate results and thus be responsible for assay failures. Likewise, a defective washing protocol can lead to carryover errors that give false readings for a large number of assay results involving a large number of samples. This can be caused by adherence of dispensed fluid to the delivery vessel (e.g., probe or proboscis). Alternatively, where the vessel contacts reagent or diluent it can lead to over diluted and thus under reported results. Entrainment of air or other fluids to a dispensed fluid can cause the volume of the dispensed fluid to be below specification since a portion of the volume attributed to the dispensed fluid is actually the entrained fluid.
One method of ameliorating failures is through the detection of system errors. Once detected, unacceptable results can be appropriately dealt with or discarded and can prevent an improper clinical choice. The instrument can be made to provide an error message, discard unreliable results, cease further processing of a sample assay, perform an additional or confirmatory assay, or conduct further instrument diagnostics by virtue of mechanical/hardware systems or by software driven protocols for dealing with the errors. For example, liquid level sensors are frequently used to detect whether sample volume dispensed into a cuvette is sufficient to conduct an assay. The volume of cuvette contents is then incorporated into an algorithm within the system software. If it falls below a predetermined level, the algorithm instructs the instrument not to continue the assay and to generate a message indicating that the sample volume is insufficient to conduct the test requested for that sample.
Indeed, given enough effort, it is often possible to consider a range of possible detection schemes to attack targeted failure modes. Within a given set of possible detection schemes various degrees of robustness or probability of detecting the failure mode will be available. Setting the detection threshold high enough to catch all failures will likely catch many non-failures. This is a serious system reliability and cost concern. In an emergency room environment, failure to report a result promptly can put the patient at risk. Thus, a dilemma can arise in which failure modes can be determined as well as a range of possible detection schemes for each of the failure modes yet implementation of each detection mode is also unacceptable. Selecting the proper combination of failure detection modes to reduce failure rates to an acceptable level with a concomitant low level of risk that an acceptable result will not be discarded is thus a formidable challenge.
U.S. Pat. No. 5,646,049 describes a system of scheduling operations in an automated analyzer. Sources of carryover and contamination are analyzed and addressed by sequencing various steps (e.g., pipetting and wash steps) in the analysis scheme to minimize their impact. The software establishes a matrix to identify when carryover or contamination is likely, based upon preceding and succeeding pipetting steps scheduled by the scheduler software. The apparatus and method, based upon values from the matrix corresponding to the preceding and succeeding pipetting steps, causes the analytical system to respond with appropriate wash characteristics to eliminate the possibility of undesirable carryover or contamination when they appear likely. The matrix identifies whether or not a step is likely to introduce an error such as carryover. There is no quantification of the probability that such an error will occur and the probability that the error will have particular clinical significance or the extent of the error. In other words, potential error sources are addressed as individual events and not as having a compounding effect.
Under the FDA's Quality System Regulations for Medical Devices, developers of diagnostic equipment must go through a process to identify and rank potential system hazards, assess probability and severity, mitigate the high level hazards and quantify the new level of the mitigated risk. Hazard Analysis and Failure Modes and Effects Criticality Analysis are tool commonly used in the industry. Redesign of the system may be able to reduce the probability of a risk to a level where the possibility of occurrence is extremely remote. Often, it is necessary to add systems to detect the occurrence of the failure and prevent the reporting of the result associated with that occurrence.
The typical practice in the diagnostic industry is to design a detection system to detect a specific failure mode. Occasionally, the detection system may detect multiple failure modes, but that situation is more by good fortune rather than by intent. If the effect of a failure mode is an assay failure as defined earlier, the detection system must be tuned to a very high level of effectiveness. This tuning usually leads to serious difficulties.
Firstly, the detection system may cause potentially good results to be discarded. A population of the parameter being monitored, e.g. distance, volume, voltage, will likely exhibit a normal, Gaussian distribution with the aim point for the parameter, hopefully, at the center of the distribution. In a typical system, the parameter could vary around the set point by plus or minus 3 or 4 standard deviations without negatively effecting the final result. The detection system will also have some inherent measurement error or noise, such that repeated measurements of the same parameter will also form a normal distribution with the center of the distribution, hopefully, at the desired detection limit for the parameter. In almost all cases, the detection system must perform its function without effecting the parameter. For instance, volume measurements must take place without contaminating the fluid, temperature measurements must not alter the temperature of the object being monitored. Also, the detection system must work within timing and cost constraints of the instrument. The net effect of these limitations is that the measurement error may be significantly higher than may be desired. In other words, the normal curve represented by the detection system is wide. When the placement of the detection limit relative to the parameter set point causes the two normal distributions to overlap, the area under the intersection of the curves represents the cases where parameters that could yield valid results will be discarded by the detection system. The cost of achieving a high level of effectiveness for the detection system is a high number of discarded good results.
Secondly, the cumulative effect of multiple detection systems with respect to false positives, i.e. discarded good results, is additive. The more detection systems within a diagnostic instrument, the higher the number of false positives. If each significant failure mode had a separate detection system, the total number of detection systems would be high. In many cases, a detection system for a particular failure mode would require additional hardware. This adds more cost and further-degrades instrument reliability.
Thirdly, another consideration is the cost of developing highly effective detection systems. For example to verify that a detection system has achieved a effectiveness of 99.9% or better, a run of more than 4600 tests must be free of any of the errors being evaluated. Preferably, this test should be performed on multiple instruments. Verifying higher levels of effectiveness can become impractical in terms of time and cost.
It can be seen that it would be highly desirable to achieve high detection effectiveness by some other way than by attacking each failure mode individually.
The accuracy and precision of automated clinical analyzers can be improved by considering the cooperative effects of improvements in the sample and reagent manipulation systems and implementing those improvements in the components of those systems. Such an approach would also suggest new operations to reduce errors where the optimization of existing processes (e.g., probe wash steps) would not reduce the probability or extent of an error to the required degree. It would also suggest such new operations where the probability or extent of multiple errors could be better addressed by a nonexistent operation.